NICE visual cookbook for R graphics
Impact team
How to create NICE style graphics
The niceRplots package and cookbook make the process of
creating publication-ready graphics in the NICE style a more
reproducible process, as well as making it easier for people new to R to
create graphics.
Within this cookbook, we will demonstrate how to use the functions
included within the niceRplots package. We will also
provide examples of plots that have been created in the NICE style.
Load the relevant packages
Before we can start making charts, we first need to load all of the
relevant packages. We won’t load every package here, just the ones that
we will use the most often. It is good practice to limit the number of
loaded packages to prevent masking conflicts. For this reason, we will
often use double colons :: to access specific functions
within a package (e.g. readr::read_csv()), rather than
loading the full package using library(pkg).
# Load in the relevant packages
library(dplyr)
library(ggplot2)
library(leaflet)
library(plotly)
devtools::load_all()
# Set up the NICE colour palette
nice_colours <- c("#228096",
"#D07B4D",
"#37906D",
"#00436C",
"#EAD054")How to style your charts
As an example we will create a simple bar chart using the
iris dataset. We will first create a basic chart, and then
add some additional formatting. We can see the two charts below with and
without formatting.
# Wrangle data for plotting
iris_bar_df <- iris %>%
group_by(Species) %>%
summarise(Sepal_Width = mean(Sepal.Width)) %>%
ungroup()
# Create basic bar chart (left)
iris_bar <- ggplot(iris_bar_df) +
geom_col(aes(x = Species, y = Sepal_Width), ) +
labs(title = "Basic chart title",
subtitle = "Basic chart subtitle",
x = "Species",
y = "Sepal Width")
# Create formatted chart (right)
iris_bar_formatted <- ggplot(iris_bar_df) +
# Adjust bar fill and outline colour
geom_col(aes(x = Species, y = Sepal_Width), fill = nice_colours[1], colour = "#000000")+
# Add baseline to x axis
geom_hline(yintercept = 0, linewidth = 1, colour = "#333333") +
# set y-axis limits and remove padding
scale_y_continuous(expand = c(0, 0), limits = c(0, 4)) +
# Add labels
labs(title = "Formatted chart title",
subtitle = "Formatted chart subtitle ",
y = "Sepal width")
iris_bar
iris_bar_formatted
We can now apply the NICE theme using the
nice_gg_theme() function from the niceRplots
package, specifying the base font for the plot to be of size 12. The
nice_gg_theme() function alters several aspects of the
chart to make it consistent with the NICE brand
guidelines. These are as follows:
Fonts: changes the title font to Lora SemiBold, and all other text to Inter Regular.
Text scaling: applies a text hierarchy to the chart using relative text sizes. Changing the optional
base_sizeargument will change the text size throughout the plot. Titles and subtitles will be automatically scaled relative to this base_size to maintain the text hierarchy. This is useful if you want to change the chart size, as all text can be simply adjusted by changing thebase_sizeargument.Colour scheme: changes line and text colours to be consistent with the NICE brand colour palette. This is particularly noticeable when creating faceted charts (shown later in cookbook). It should be noted that this will not add colour to the plotted data, this will still need to be added manually when creating the chart. For instance, a fill colour was added in the
geom_col()argument in the above example.Margins: adjusts the margins around the title, subtitle, and axis titles to give everything space to breathe.
Background: removes the panel background and borders.
Other: Applies major grid lines along the y-axis. This will be appropriate for most charts, but may need to be disabled in others by applying
theme(panel.grid.major.y = ggplot2::element_blank())
The nice_gg_theme() function isn’t designed to modify
every aspect of the chart, but to apply a general theme that will
maintain consistency with the NICE style guide across all charts. Many
aspects of the chart will still need to be modified manually. For
example, in the below chart we may choose to apply an additional theme
argument to remove the x-axis title.
iris_bar_themed <- iris_bar_formatted +
# Apply NICE theme, set base text size to 12
nice_gg_theme(base_size = 12) +
# remove x axis title as this is not needed
theme(axis.title.x = ggplot2::element_blank())
iris_bar_themed
How to finalise your charts
Once a plot has been created and styled using
nice_gg_theme(), the next step is to apply the
finalise_plot() function. This function will create a
footer containing information on the data source, as well as adding the
NICE logo to the bottom right. It will then left-align the title,
subtitle and footer. The finalise_plot() function has 3
arguments:
plot_name: the name of the plot object, in the example below we have saved ours asiris_bar_themed.source_name: the source of the underlying data. We have used data from theirisdataset.logo: this will determine whether the NICE logo is included in the footer. It can have a value of “NICE” to include the logo, or “none” to leave it blank.
# Apply the finalise plot function
finalised_plot <- finalise_plot(iris_bar_themed,
source_name = "Iris dataset",
logo = "NICE")
finalised_plot
How to save your finalised plots
After applying the finalise_plot() function, the chart
can be saved using the ggsave() function.
# Save plot using the ggsave() function
ggsave("finalised_plot.png", finalised_plot)Static charts using ggplot2
The sections below provide examples of how to make different types of
static chart using the ggplot2 package. These will demonstrate how to
format different charts and apply the NICE theme. For some examples we
will continue to use R’s built in iris dataset. For others
we will use data on the monthly Sub-ICB location level prescribing of 5
direct oral anticoagulants (DOACS) in England between February 2017 and
January 2022. This was downloaded from OpenPrescribing.net.
# Load in DOACs data
doacs_df <- readr::read_csv(here::here("DOACs_data.csv"),
col_types = "Dcccccdddd")Histogram
# Create chart
nice_hist <- iris %>%
ggplot() +
geom_histogram(aes(x = Sepal.Width, fill = Species),
binwidth = 0.2, color = "black") +
# Add a line on the x axis
geom_hline(yintercept = 0, linewidth = 1, colour = "#333333") +
# Remove extra spacing and set y axis limits
scale_y_continuous(expand = c(0,0), limits = c(0,40)) +
# Use NICE colours
scale_fill_manual(values = nice_colours) +
# Add NICE theme and set labels
nice_gg_theme() +
labs(title = "Species setosa has the longest sepal length",
subtitle = "Distribution of sepal width by species",
x = "Sepal Width",
y = "Frequency")
nice_hist
#finalise_plot(example_plot, source_name = "Source: Iris dataset")Bar chart
# Prepare data, get total items dispensed for each medicine in 2021 (in millions)
bar_df <- doacs_df %>%
filter(between(date, as.Date("2021-01-01"), as.Date("2021-12-31"))) %>%
group_by(chemical) %>%
summarise(items = sum(items)/1000000) %>%
ungroup()
# Create chart
bar_chart <- bar_df %>%
# Plot bars in decreasing order of items
ggplot(aes(y = items, x = reorder(chemical, -items))) +
# Add baseline to x axis
geom_hline(yintercept = 0, linewidth = 1, colour = "#333333") +
# set y-axis limits and remove padding
scale_y_continuous(expand = c(0,0), limits = c(0,8)) +
# set x-axis labels to wrap to prevent overlapping
scale_x_discrete(labels = function(x) {stringr::str_wrap(x, width = 10)}) +
# Add bars, add fill and border colours
geom_col(fill = nice_colours[1], colour = "#000000") +
# Apply NICE theme
nice_gg_theme(base_size = 12) +
# Remove the x-axis title
theme(axis.title.x = ggplot2::element_blank()) +
# Add labels
labs(title = "Apixaban was the most prescribed DOAC in 2021",
subtitle = "Total DOAC medicines dispensed in primary care in England, 2020",
y = "Dispensed items (millions)")
bar_chart
Line chart
# Prepare data
line_df <- doacs_df %>%
filter(chemical == "Edoxaban") %>%
group_by(date, chemical) %>%
summarise(items = sum(items)) %>%
ungroup()
# Create chart
line_chart <- line_df %>%
ggplot(aes(x = date, y = items)) +
# Add baseline to x axis
geom_hline(yintercept = 0, linewidth = 1, colour="#333333") +
# Add the vertical dashed line to show lockdown date. Added this before plotting the
# data so that it sits on a layer behind the line and points
geom_vline(xintercept = as.Date("2020-03-01"), linewidth = 0.6, linetype = "dashed") +
# Add the line and points, make both teal
geom_line(linewidth = 0.8, colour = nice_colours[1]) +
geom_point(size = 2, colour = nice_colours[1]) +
# Set y-axis limits and remove padding, add commas to axis labels
scale_y_continuous(expand = c(0,0), limits = c(0,200000), label = scales::comma) +
# Set x-axis labels to show as month year, and to occur at 6-monthly intervals
scale_x_date(date_labels = "%b\n%Y", date_breaks = "6 months") +
# Apply NICE theme
nice_gg_theme() +
# Remove x-axis title
theme(axis.title.x = ggplot2::element_blank()) +
# Add labels
labs(title = "Edoxaban prescribing has increased since 2017",
subtitle = "Edoxaban prescribing in England, 2017-2022",
y = "Dispensed items") +
# Add annotation for dashed line
annotate("text", x = as.Date("2020-02-01"), y = 180000,
label = "National lockdown\non 23 March", hjust = "right")
line_chart
Scatter plot
# Prepare data
scatter_df <- doacs_df %>%
filter(chemical == "Apixaban",
date == "2021-07-01") %>%
mutate(total_list_size = total_list_size/1000)
# Create chart
scatter_chart <- scatter_df %>%
ggplot(aes(x = total_list_size, y = items)) +
# Add the points and make them teal
geom_point(colour = nice_colours[1], size = 2) +
# set x-axis limits, adjust padding and add commas to axis labels
scale_x_continuous(expand = expansion(mult = c(0, 0.05)),
limits = c(0,3000), label = scales::comma) +
# set y-axis limits and remove padding, add commas to axis labels
scale_y_continuous(expand = c(0,0), limits = c(0, 25000), label = scales::comma) +
# Apply NICE theme
nice_gg_theme() +
# Add a panel border and vertical grid lines
theme(panel.border = ggplot2::element_rect(color = "#000000",
linewidth = 0.3,
fill = NA),
panel.grid.major.x = ggplot2::element_line(color = "#BFBFBF")) +
# Add labels
labs(title = "Apixaban prescribing",
subtitle = "Items of apixaban dispensed in primary care compared to\nsub-ICB location list size, July 2021",
x = "Primary care list size (x1000)",
y = "Dispensed items")
scatter_chart
Faceted chart
# Prepare data
facet_df <- doacs_df %>%
filter(chemical %in% c("Apixaban", "Edoxaban", "Warfarin sodium"),
between(date, as.Date("2020-01-01"), as.Date("2021-12-31"))) %>%
group_by(date, chemical) %>%
summarise(items = sum(items)/1000) %>%
ungroup()
# Create chart
facet_chart <- facet_df %>%
ggplot(aes(x = date, y = items, color = chemical)) +
# Add lines
geom_line(linewidth = 1) +
# Add points
geom_point(size = 2) +
## set y-axis limits and remove padding
scale_y_continuous(expand = c(0,0), limits = c(0,800)) +
# set x-axis labels to show as month year, and to occur at 6-monthly intervals
scale_x_date(date_labels = "%b\n%Y", date_breaks = "6 months") +
# Manually set the colour of the lines and points
scale_color_manual(values = nice_colours) +
# APply facet wrapping to put each chemical into an individual plot
facet_wrap(~chemical) +
# Apply the NICE theme
nice_gg_theme() +
# Add a panel border, remove x axis title and legend
theme(panel.border = ggplot2::element_rect(color = "#000000",
linewidth = 0.3,
fill = NA),
axis.title.x = ggplot2::element_blank(),
legend.position = "none") +
# Add labels
labs(title = "Trends in anti-coagulant prescribing",
subtitle = "Prescribing of 3 anti-coagulants in England, 2020-2021",
x = "Sepal Length",
y = "Dispensed items (thousands)")
facet_chart
Choropleth map (heatmap)
Prepare .geoJSON files
In the example below we will prepare a choropleth map of Apixaban
prescribing across Sub-ICB locations in 2021. To prepare this chart we
need two files, a dataframe containing the data we want to visualise,
and a dataframe containing the shapes for our relevant health
geographies. For these examples we will use the doacs_df
and sub_icb_shapes_2022 files that come built into the
niceRplots package.
There will be many situations where you will want to visualise data
across other geographies. The files containing shapes for other
geographies can be downloaded in .geoJSON format from the
Office for National Statistics (ONS) Open Geography
Portal.
These .geoJSON files from ONS come with different levels
of accuracy, with more accurate files having a larger file size. These
files can be distinguished by the letters at the end of the file name
(BFC > BFE > BCG > BUC). We recommend downloading the smallest
.geoJSON file (BUC), as this will greatly increase the
speed of plotting. In some cases only a very detailed file will be
available. In these cases you will need to simplify the file using the
rmapshaper package.
The code below provides examples of how to load in a
.geoJSON file as a dataframe, how to load and simplify a
file using the rmapshaper package, and how to combine
multiple shapes into a single shape (useful to give the map a darker
outside boundary).
# Load in the a .geoJSON file containing the relevant geographical shapes
sub_icb_shapes_2022 <- sf::read_sf("EXAMPLE_SHAPES.geojson")
# Load in the a .geoJSON file containing the relevant geographical shapes and simplify
# these to increase plotting speed. In the code below, we are keeping 5% of the total points.
sub_icb_shapes_2022 <- sf::read_sf("EXAMPLE_SHAPES.geojson") %>%
ms_simplify(keep = 0.05)
# Combine these shapes into a single shape for the whole of England.
# Setting the sf_use_s2() function to FALSE prevents the use of spherical geometry
sf::sf_use_s2(FALSE)
england_shape_2022 <- sub_icb_shapes_2022 %>%
sf::st_union()Now that we have prepared our .geoJSON files, we can go
ahead and prepare our choropleth map.
# Prepare the data
map_df <- doacs_df %>%
filter(between(date, as.Date("2021-01-01"), as.Date("2021-12-31")),
chemical == "Apixaban") %>%
group_by(chemical, name, ods_code, gss_code) %>%
summarise(items_per_1000 = sum(items)/(sum(total_list_size)/1000),
items = sum(items)) %>%
ungroup()
# Join our data to the dataframe containing the relevant Sub ICB location shapes. Ensure the
# dataframe containing the shapes is the first argument in the join, as we want to preserve
# the class of this table. If not the geometry column containing the shapes will be dropped.
map_df <- left_join(sub_icb_shapes_2022, map_df, by = c("sicbl22cd" = "gss_code"))
# Set up the colour palette
pal <- colorRampPalette((c("#BFBFBF", nice_colours[1])))(5) # Sequential palette
#pal <- viridis::viridis(5) #pal <- viridis::viridis(5) # Alternative sequential palette
#pal <- colorRampPalette((c("#228096", "#BFBFBF","#EAD054")))(5) # Diverging palette
# Create chart
choropleth_chart <- map_df %>%
# Set up a column with categorical labels for each bin
mutate(items_per_1000_bin = cut(items_per_1000,
breaks = c(0,5,10,15,20,Inf),
labels = c("<5","5-10", "10-15", "15-20", "20+"))) %>%
ggplot() +
# Plot the shapes. Add white borders for accesibility
geom_sf(aes(fill = items_per_1000_bin), color = "#FFFFFF", lwd = 0.05) +
# Apply our palette and adjust the legend
scale_fill_manual(values = pal,
na.value = "#808080",
guide = guide_legend(
title = "Items per 1000\npopulation",
title.hjust = 0.5,
label.position = "left",
reverse = TRUE)) +
# Add another layer with the England shape to add an outline
geom_sf(data = england_shape_2022, fill = NA, color = "black", lwd = 0.1) +
# Add the NICE theme
nice_gg_theme() +
# Alter some theme elements - remove axes, move the legend and add title
theme(legend.position = c(0.23, 0.5),
axis.text = element_blank(),
axis.ticks.x = ggplot2::element_blank(),
legend.title = ggplot2::element_text(family = "Inter Regular",
size = 12,
color = "#000000"),
legend.text.align = 1) +
# Add labels
labs(title = "Apixaban prescribing in England, 2021",
subtitle = "Plotted by sub-ICB location")
choropleth_chart
Interactive charts
Plotly
The Plotly package can be used to
create interactive charts in R. Like with nice_gg_theme()
and ggplot, the niceRplots package contains the
nice_plotly_theme() function which can be used to apply a
NICE theme to a Plotly chart.
The nice_plotly_theme() function alters the following
aspects of the chart to make it more consistent with the NICE brand
guidelines:
Fonts: changes all text to Inter Regular and sets the minimum font size to the
font_sizeargument (default is12).Grid lines: the horizontal and/or vertical grid lines are displayed according to the graph type selected in the
chart_typeargument (options are"vertical_bar","horizontal_bar","scatter” and"line").Margins: adds space between the axis lines and the tick labels. The default Plotly theme has no axis tick marks, just tick labels, and this function adds a gap between the axis lines and the tick labels for better spacing. If tick marks are desired, turn the padding off with
pad_axes = FALSE, otherwise there will be a gap between the tick marks and the axis lines.Modebar: removes all modebar buttons from the chart, e.g. zoom, select, pan, except for the camera icon, which allows the graph to be saved as a PNG.
Like with nice_gg_theme(), you will need to manually
specify the use of NICE colours in the graph elements, e.g. with
line = list(color = "#228096") for line charts or
marker = list(color = "#228096", line = list(color = "#000000"))
for bar charts.
nice_plotly_theme() only makes basic adjustments to make
graphs more aesthetically consistent. You may need to make additional
changes.
Styling a Plotly chart
Using the iris dataset, we first create a basic chart
(top), then recreate the chart with a NICE colour applied to the fill
colour of the bars and adding a black border to the bars (bottom).
We also alter how the x and y values are displayed in the hover text
(or tooltip) using hovertemplate. See the Plotly guidance
on hover text and formatting in R for more information.
# Create basic bar chart (top)
plotly_iris_bar <- plot_ly(iris_bar_df,
x = ~Species,
y = ~Sepal_Width,
type = "bar")
# Create formatted chart (bottom)
plotly_iris_bar_formatted <- plot_ly(iris_bar_df,
x = ~Species,
y = ~Sepal_Width,
type = "bar",
marker = list(color = nice_colours_full[["bold_teal_100"]],
line = list(color = nice_colours_full[['black_100']], width = 1.5)),
hovertemplate = paste0("Species: %{x}<br>",
"Sepal width: %{y:.2f}",
"<extra></extra>"))
plotly_iris_barplotly_iris_bar_formatted# fig.show = "hold" not working for Plotly?We then apply the NICE Plotly theme using
nice_plotly_theme(), providing axis titles and specifying
chart_type = "vertical_bar".
plotly_iris_bar_themed <- plotly_iris_bar_formatted %>%
nice_plotly_theme(chart_type = "vertical_bar",
x_title = "Species",
y_title = "Sepal Width")Formatted chart headline title
Figure x. Formatted chart statistical title
[Alt text - text description of message chart is showing]
Source: R - Iris dataset
Download the data for Figure x (CSV,
5.0KB).
How to save your plot
To download a Plotly plot as a static PNG image, click on the camera icon at the top right of the plot.
Saving in other file formats (e.g. .svg or .jpg) is more complicated and requires installation of another piece of software. Read the Plotly documentation on exporting graphs as static images in R.
Chart types
Histogram
Use chart_type = "vertical_bar" for histograms.
# Create chart
plotly_hist <- iris %>%
plot_ly(x = ~Sepal.Width,
color = ~Species,
# Use NICE colours - remove colour names so it maps correctly
colors = unname(nice_colours[1:3]),
type = "histogram",
# Black outline for bars
marker = list(line = list(color = nice_colours_full[['black_100']], width = 1.5)),
# Set bin width
xbins = list(size = 0.2,
start = 1.9,
end = 4.5),
# Edit hover text
hovertemplate = paste0("Sepal width: %{x}<br>",
"Count: %{y}")) %>%
# Stack bars
layout(barmode = "stack") %>%
# Add NICE theme and set axis titles
nice_plotly_theme(chart_type = "vertical_bar",
x_title = "Sepal width",
y_title = "Count")
plotly_hist# Category order differs from ggplot but no big differenceBar chart
Vertical bar chart
Use chart_type = "vertical_bar".
Add a black outline around the bars so adjacent colours of stacked bars are better distinguished.
# Create chart
plotly_vbar_chart <- bar_df %>%
# Reorder chemical factor levels in decreasing order of items
mutate(chemical = reorder(chemical, -items)) %>%
plot_ly(x = ~chemical,
y = ~items,
type = "bar",
# Bold teal bars with black outline
marker = list(color = nice_colours_full[["bold_teal_100"]],
line = list(color = nice_colours_full[['black_100']], width = 1.5)),
# Edit hover text
hovertemplate = paste0("%{x}<br>",
"%{y:.2f} million items dispensed",
"<extra></extra>")) %>%
# Add NICE theme and set axis titles
nice_plotly_theme(chart_type = "vertical_bar",
x_title = "",
y_title = "Dispensed items (millions)")Apixaban was the most prescribed DOAC in 2021
Figure x. Total DOAC medicines dispensed in primary care in England, 2020
Horizontal bar chart
Use chart_type = "horizontal_bar".
# Create chart
plotly_vbar_chart <- bar_df %>%
# Reorder chemical factor levels in decreasing order of items
mutate(chemical = reorder(chemical, items)) %>%
plot_ly(x = ~items,
y = ~chemical,
type = "bar",
# Make horizontal
orientation = "h",
# Bold teal bars with black outline
marker = list(color = nice_colours_full[["bold_teal_100"]],
line = list(color = nice_colours_full[['black_100']], width = 1.5)),
# Edit hover text
hovertemplate = paste0("%{y}<br>",
"%{x:.2f} million items dispensed",
"<extra></extra>")) %>%
# Add NICE theme and set axis titles
nice_plotly_theme(chart_type = "horizontal_bar",
x_title = "Dispensed items (millions)",
y_title = "")
plotly_vbar_chartLine chart
Use chart_type = "line".
pad_axes = FALSE here in
nice_plotly_theme() as we do not want a gap between the x
axis tick marks and the x axis line.
# Create chart
plotly_line_chart <- line_df %>%
plot_ly(x = ~date,
y = ~items,
type = "scatter",
# Line with marker dot at each data point
mode = "lines+markers",
# Make line and dots teal
marker = list(color = nice_colours_full[["bold_teal_100"]]),
# Edit hover text. Use hovertext instead of hovertemplate as date
# tick labels edited to show month and year in two separate lines,
# layout(tickformat = "%b\n%Y"), but don't want new line for year in
# hover text
hoverinfo = "text",
hovertext = ~paste0(format(date, "%b %Y"),
"<br>Dispensed items: ",
scales::label_comma()(items))) %>%
# Add the vertical dashed line to show lockdown date. Added this before plotting the
# data so that it sits on a layer behind the line and points
add_lines(x = as.Date("2020-03-01"),
y = ~c(0, max(items)),
line = list(dash = "dash",
color = "#000000",
width = 1.5),
# Remove hover text
hoverinfo = "none",
# Don't inherit properties of previous trace
inherit = FALSE) %>%
layout(showlegend = FALSE,
xaxis = list(type = "date", # Specify x axis is date
# Show x axis ticks
ticks = "outside",
# Format ticks as abbreviated month name and full year, e.g. Jan 2018
tickformat = "%b\n%Y",
# Set first tick
tick0 = "2017-07-01",
# Tick every 6 months
dtick = "M6"),
# Y axis ticks with commas as thousands separators
yaxis = list(tickformat = ",",
range = ~c(0, max(items)+10000)),
annotations = list(x = as.Date("2020-03-01"),
xshift = -60,
y = 180000,
text = "National lockdown\non 23 March",
xref = "x",
yref = "y",
showarrow = FALSE,
align = "right")) %>%
# Add NICE theme and set axis titles
nice_plotly_theme(chart_type = "line",
x_title = "",
y_title = "Dispensed items",
# Don't pad the axes, as ticks shown. Do not want gap between axis line and ticks
pad_axes = FALSE)Edoxaban prescribing has increased since 2017
Figure x. Edoxaban prescribing in England, 2017-2022
Scatter plot
Use chart_type = "scatter".
# Create chart
plotly_scatter_chart <- scatter_df %>%
plot_ly(x = ~total_list_size,
y = ~items,
type = "scatter",
mode = "markers",
marker = list(color = nice_colours_full[["bold_teal_100"]]),
hoverinfo = "text",
hovertext = ~paste0("List size: ", scales::label_comma()(total_list_size*1000),
"<br>", scales::label_comma()(items), " items dispensed")
) %>%
layout(showlegend = FALSE,
# Axis ticks with commas as thousands separators
xaxis = list(tickformat = ","),
yaxis = list(tickformat = ",")) %>%
# Add NICE theme and set axis titles
nice_plotly_theme(chart_type = "scatter",
x_title = "Primary care list size (x1000)",
y_title = "Dispensed items",
# Don't pad the axes, as ticks shown. Do not want gap between axis line and ticks
pad_axes = FALSE)Apixaban prescribing
Items of apixaban dispensed in primary care compared to sub-ICB location
list size, July 2021
Faceted charts
The easiest way to make NICE-themed faceted charts in Plotly
is to make it in ggplot, apply the nice_gg_theme() and
then convert it into a Plotly chart using the function ggplotly.
# Create chart
ggplotly(facet_chart)It is possible to do it entirely in Plotly using subplots, however, formatting the facet labels is challenging.
Choropleth map (Leaflet)
# Using same data as above
# Create chart
pal <- colorBin("plasma",
domain = map_df$items_per_1000,
bins = 5,
na.color = "#808080")
leaflet_map <- map_df %>%
# Set up width and degree of zoom per click
leaflet(width = 800,
options = leafletOptions(zoomDelta = 0.25,
zoomSnap = 0.25)) %>%
# Set view to centre on England
setView(lat = 53,
lng = -1.5,
zoom = 6) %>%
# Add basic base tile
addProviderTiles(provider = "CartoDB.Positron") %>%
# Add shapes
addPolygons(fillColor = ~pal(items_per_1000),
fillOpacity = 0.3,
color = "#393939",
weight = 1.5,
opacity = 1,
layerId = ~ods_code,
# Add functionality to highlight shape when hovered over
highlight = highlightOptions(weight = 3,
color = "#222222",
fillOpacity = 0.7,
bringToFront = TRUE),
# Add labels upon hover
label = ~lapply(paste0("<strong>Name: </strong>", sicbl22nm,
"<br><strong>Items per 1000 population: </strong> ",
round(items_per_1000, 2)),
htmltools::HTML),
labelOptions = labelOptions(textsize = "12px",
style = list("font-family" = "Arial"))) %>%
clearControls() %>%
addLegend(position = "bottomleft",
pal = pal,
values = ~items_per_1000,
title = "Items per 1000 population",
opacity = 0.7)
leaflet_map